code: def run_multiple_example(
      path_to_file,
      path_to_exog_file,
      out_path=None,
      visualization=False,
      len_forecast=40,
  ):
      mse_errors_df = create_errors_df()
      mae_errors_df = create_errors_df()
      mape_errors_df = create_errors_df()
  
      path_to_file = path_to_file
      path_to_exog_file = path_to_exog_file
  
      df = pd.read_csv(path_to_file)
      df_exog = pd.read_csv(path_to_exog_file)
  
      for point in df.columns.values:
          if point != "dates":
              time_series = df[point]
              exog_variable = df_exog[point]
  
              errors = run_nemo_based_forecasting(
                  time_series=time_series,
                  exog_variable=exog_variable,
                  len_forecast=len_forecast,
                  visualization=visualization,
              )
  
              mse_errors_df = add_data_to_errors_df(
                  mse_errors_df, "MSE", point, errors
              )
              mae_errors_df = add_data_to_errors_df(
                  mae_errors_df, "MAE", point, errors
              )
              mape_errors_df = add_data_to_errors_df(
                  mape_errors_df, "MAPE", point, errors
              )
  
      if out_path is not None:
          mse_errors_df.to_csv(
              os.path.join(out_path, "mse_errors.csv"), index=False
          )
          mae_errors_df.to_csv(
              os.path.join(out_path, "mae_errors.csv"), index=False
          )
          mape_errors_df.to_csv(
              os.path.join(out_path, "mape_errors.csv"), index=False
          )
  
      if visualization:
          boxplot_visualize(mse_errors_df, "MSE")
          boxplot_visualize(mae_errors_df, "MAE")
          boxplot_visualize(mape_errors_df, "MAPE")
  
imports: import os
  from copy import deepcopy
  import numpy
  import pandas
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TsForecastingParams, TaskTypesEnum
label_desc: Use image node_id 15 for calling a global function with example usage: run_multiple_example(path_to_file, path_to_exog_file, out_path, visualization, len_forecast) without return types